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About This Role
Job Description:
At Bank of America, we are guided by a common purpose to help make financial lives better through the power of every connection. We do this by driving Responsible Growth and delivering for our clients, teammates, communities and shareholders every day.
Being a Great Place to Work and providing a culture of caring is core to how we drive Responsible Growth. We are intentional about fostering an inclusive workplace where every teammate has the opportunity to succeed, build a career and contribute to our shared success. This includes attracting and developing exceptional talent, recognizing and rewarding performance, and supporting our teammates’ physical, emotional, and financial wellness through affordable, competitive and flexible benefits.
We value the unique perspectives individuals bring from all backgrounds and career paths \- whether shaped by military service, community college education, or a wide range of work and life experiences. These journeys foster resilience, leadership and innovation, strengthening our workforce and positively impact the communities we serve.
Bank of America is committed to an in\-office culture that supports collaboration, engagement, and career development. Our approach includes clear in\-office expectations, while providing an appropriate level of flexibility based on role\-specific responsibilities and business needs.
At Bank of America, you can build a successful career with opportunities to learn, grow, and make an impact. Join us!
Job Description:
Shape the next generation of AI\-powered banking inside Global Payments Solutions by turning data, automation, and GenAI capabilities into trusted products that improve how GPS sells. serves, decides, and operates globally. This role will focus on high\-value AI use cases that drive revenue enablement, productivity, client intelligence, decision quality, risk discipline, and adoption across markets, teams, and client segments.
Responsibilities \- Representative Projects
- Develop AI\-enabled capabilities that automate client reviews, personalize sales recommendations, synthesize client feedback, improve billing workflows, and accelerate self\-service insights through governed production delivery.
- Prioritize and convert a broad AI opportunity pipeline into execution\-ready use cases by applying value, feasibility, governance, and adoption criteria across responsible growth, operational efficiency, and client experience.
- Deliver measurable productivity, revenue enablement, and risk reduction outcomes by embedding AI into high\-volume workflows such as treasury reviews, sales preparation, billing validation, client servicing, and operational controls.
- Increase adoption and business confidence by pairing AI delivery with clear success metrics, stakeholder engagement, model governance, user feedback loops, training, and executive\-ready value tracking.
- Design prompt frameworks, retrieval strategies, agentic workflows, and user experiences that improve accuracy, relevance, trust, usability, and adoption.
- Translate AI and data investments into executive\-ready business cases, ROI frameworks, adoption metrics, and narratives tied to client value, revenue growth, productivity, and risk reduction.
Your Role:
In this role you will shape the Data and AI strategy for Global Payment Solutions and influence investment priorities across senior leadership teams. You will identify high\-value use cases, define solution patterns, establish operating discipline, and guide delivery from concept through global adoption.
This is a senior change\-agent role for a leader who can secure sponsorship, align competing priorities, and lead through ambiguity. The successful candidate can move from executive strategy to hands\-on problem solving and turn emerging AI capabilities into scalable, governed, commercially relevant business products.
You will serve as the bridge between senior business priorities and technical delivery, helping teams focus on the highest\-value AI opportunities, manage risk and controls early, and create repeatable adoption patterns across Global Payment Solutions globally.
Additional Responsibilities:
- Shape the long\-term Data and AI strategy for Global Payments Solutions, including product vision, roadmap, investment priorities, operating model, and success metrics tied to client value, revenue, productivity, risk reduction, and adoption.
- Build AI product management practices, including prompt libraries, reusable workflows, intake routines, governance forums, adoption playbooks, value measurement, and responsible AI controls.
- Lead product discovery and solution design, including prompt frameworks, retrieval strategies, payments data requirements, user journeys, business cases, client feedback, and production readiness plans.
- Influence executive decision\-making by presenting clear tradeoffs, risk considerations, adoption progress, measurable outcomes, and investment recommendations.
- Drive execution across Product, Technology, Data Science, Risk, Compliance, Legal, Operations, Sales, and client\-facing teams, removing blockers, managing tradeoffs, and accelerating delivery from strategy to global adoption.
Required Skills:
- 6 years of experience across AI transformation, data product management, technology delivery, payments, treasury services, transaction banking, cash management, receivables, or financial services, with demonstrated success launching scalable products in complex enterprise environments.
- Hands\-on experience designing, testing, and optimizing prompts, AI enabled user experiences, RAG patterns, agentic workflows, and GenAI capabilities, with strong understanding of mode behavior, accuracy, adoption, controls, and responsible use.
- Strong data, technology, and payments foundation, including experience partnering with engineers, architects, data scientists, analytics teams, platform owners, governance, risk, and control partners to deliver production\-ready solutions.
- Proven ability to influence senior executives, engage clients, secure sponsorship, shape investment priorities, and drive adoption across matrixed Product, Technology, Risk, Compliance, Legal, Operations, Sales and client teams.
- Commercially minded, execution\-focused leader with strong judgement, communication skills, prioritization discipline, and ability to develop talent, standards, operating models, and reusable frameworks across and AI product portfolio.
- Bachelor's Degree required.
Desired Skills:
- Experience translating AI and data investment into executive\-ready business cases, ROI frameworks, adoption metrics, and narratives tied to client value, revenue growth, productivity, and risk reduction.
- Experience increasing adoption and business confidence through clear success metrics, stakeholder engagement, model governance, user feedback loops, training, and executive\-ready value tracking.
- Experience improving client and associate experience by reducing manual search, analysis, content creation, and workflow handoffs.
Skills:
- Agile Practices
- Application Development
- DevOps Practices
- Technical Documentation
- Written Communications
- Artificial Intelligence/Machine Learning
- Business Analytics
- Data Visualization
- Presentation Skills
- Risk Management
- Policies, Procedures, and Guidelines Management
- Adaptability
- Collaboration
- Consulting
- Networking
Shift:
1st shift (United States of America)Hours Per Week:
40
Salary Context
This $129K-$232K range is below the median for AI Product Manager roles in our dataset (median: $188K across 140 roles with salary data).
View full AI Product Manager salary data →Role Details
About This Role
AI Product Managers define what AI features get built and why. They translate business problems into ML-solvable tasks, work with engineering to scope model requirements, and own the metrics that determine if an AI feature is working. The role requires a rare combination of technical fluency and product instinct.
Unlike traditional product management, AI PM work involves managing uncertainty at a fundamental level. Your model might work 90% of the time. What happens the other 10%? What's the user experience when the AI is wrong? How do you measure 'good enough' for a probabilistic system? These questions don't have easy answers, and the AI PM is the person responsible for finding them.
Across the 3,708 AI roles we're tracking, AI Product Manager positions make up 5% of the market. At Bank of America, this role fits into their broader AI and engineering organization.
AI Product Manager roles are growing as companies realize that shipping AI features requires different product thinking than traditional software. The best candidates combine product management experience with enough technical depth to have productive conversations with ML engineers about model capabilities and limitations.
What the Work Looks Like
A typical week includes: reviewing model evaluation results with the ML team, defining success metrics for a new AI feature, conducting user research on how customers respond to AI-generated outputs, writing product requirements that include accuracy thresholds and fallback behaviors, and presenting the AI roadmap to leadership. You're the translator between technical capability and business value.
AI Product Manager roles are growing as companies realize that shipping AI features requires different product thinking than traditional software. The best candidates combine product management experience with enough technical depth to have productive conversations with ML engineers about model capabilities and limitations.
Skills Required
Technical fluency with ML concepts is essential, though you won't be writing models. Expect to understand training data, evaluation metrics, model limitations, and responsible AI practices. SQL and basic Python are increasingly expected. Experience with A/B testing, data analysis, and product analytics is baseline. Understanding LLM capabilities and limitations is now a core requirement.
The differentiator is AI-specific product thinking: knowing when to use ML vs. heuristics, understanding the cost of training data collection, designing graceful degradation for model failures, and building products that improve with usage data. Experience with AI safety, bias mitigation, and responsible AI deployment is increasingly important.
Strong postings describe specific AI products the PM will own, mention the ML team structure, and talk about measurement methodology. Look for companies that have already shipped AI features. Roles at companies that are 'exploring AI' often mean you'll spend a year defining the strategy before any building happens.
Compensation Benchmarks
AI Product Manager roles pay a median of $216,175 based on 270 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $200,000. This role's midpoint ($180K) sits 16% below the category median. Disclosed range: $129K to $232K.
Across all AI roles, the market median is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. For comparison, the highest-paying categories include AI Safety ($300,000) and Research Engineer ($280,000). By seniority level: Entry: $120,000; Mid: $200,000; Senior: $230,000; Director: $272,150; VP: $250,000.
Bank of America AI Hiring
Bank of America has 8 open AI roles right now. They're hiring across AI Software Engineer, AI Product Manager, AI/ML Engineer. Positions span Plano, TX, US, New York, NY, US, Pennington, NJ, US. Compensation range: $200K - $232K.
Location Context
AI roles in New York pay a median of $220,000 across 1,045 tracked positions.
Career Path
Common paths into AI Product Manager roles include Product Manager, Data Analyst, Technical Program Manager.
From here, career progression typically leads toward Director of AI Product, VP Product, Head of AI.
The most effective path is PM experience plus self-directed AI education. Take Andrew Ng's courses, build a small ML project, and learn enough Python to read model evaluation code. The goal isn't to become an ML engineer. It's to have credibility in technical conversations and to understand what's possible, what's hard, and what's a bad idea.
What to Expect in Interviews
AI interviews typically combine coding challenges (Python-focused), system design questions tailored to the role, and discussions about your experience with relevant tools and frameworks. Strong candidates demonstrate both technical depth and the ability to make pragmatic engineering tradeoffs. Prepare portfolio projects that demonstrate end-to-end capability rather than isolated skills.
When evaluating opportunities: Strong postings describe specific AI products the PM will own, mention the ML team structure, and talk about measurement methodology. Look for companies that have already shipped AI features. Roles at companies that are 'exploring AI' often mean you'll spend a year defining the strategy before any building happens.
AI Hiring Overview
The AI job market has 3,708 open positions tracked in our dataset. By seniority: 102 entry-level, 1,705 mid-level, 1,469 senior, and 432 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (508 positions). The remaining 3,180 roles require on-site or hybrid attendance.
The market median for AI roles is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. Highest-paying categories: AI Safety ($300,000 median, 21 roles); Research Engineer ($280,000 median, 147 roles); AI Architect ($254,798 median, 67 roles).
AI Product Manager roles are growing as companies realize that shipping AI features requires different product thinking than traditional software. The best candidates combine product management experience with enough technical depth to have productive conversations with ML engineers about model capabilities and limitations.
The AI Job Market Today
The AI job market spans 3,708 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (2,605), Data Scientist (310), AI Software Engineer (259). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (102) are outnumbered by mid-level (1,705) and senior (1,469) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 432 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (508 positions), with 3,180 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $217,500. Top-quartile roles start at $272,100, and the 90th percentile reaches $325,000. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $300,000 median, while Prompt Engineer roles sit at $140,000. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (1,890 postings), Aws (1,103 postings), Azure (877 postings), Rag (855 postings), Gcp (631 postings), Prompt Engineering (560 postings), Pytorch (545 postings), Claude (498 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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